OneDB: A Distributed Multi-Metric Data Similarity Search System

TL;DR

OneDB is a scalable, multi-metric data retrieval system that efficiently unifies diverse multi-modal data types for accurate similarity search, outperforming existing solutions in accuracy and speed.

Contribution

The paper introduces OneDB, a novel distributed system that unifies multi-modal data using multi-metric models with innovations like smart pruning, two-layer indexing, and autotuning.

Findings

Achieves 12.63%-30.75% better accuracy than state-of-the-art.

Speeds up search by 2.5-5.75 times compared to existing solutions.

Demonstrates high scalability and effective parameter autotuning.

Abstract

Increasingly massive volumes of multi-modal data are being accumulated in many {real world} settings, including in health care and e-commerce. This development calls for effective general-purpose data management solutions for multi-modal data. Such a solution must facilitate user-friendly and accurate retrieval of any multi-modal data according to diverse application requirements. Further, such a solution must be capable of efficient and scalable retrieval. To address this need, we present OneDB, a distributed multi-metric data similarity retrieval system. This system exploits the fact that data of diverse modalities, such as text, images, and video, can be represented as metric data. The system thus affords each data modality its own metric space with its own distance function and then uses a multi-metric model to unify multi-modal data. The system features several innovations: (i) an extended Spart SQL query interface; (ii) lightweight means of learning appropriate weights of different modalities when retrieving multi-modal data to enable accurate retrieval; (iii) smart search-space pruning strategies that improve efficiency; (iv) two-layered indexing of data to ensure load-balancing during distributed processing; and (v) end-to-end system parameter autotuning. Experiments on three real-life datasets and two synthetic datasets offer evidence that the system is capable of state-of-the-art performance: (i) efficient and effective weight learning; (ii) retrieval accuracy improvements of 12.63\%--30.75\% over the state-of-the-art vector similarity search system at comparable efficiency; (iii) accelerated search by 2.5--5.75x over state-of-the-art single- or multi-metric solutions; (iv) demonstrated high scalability; and (v) parameter tuning that enables performance improvements of 15+%.